CN105608681B - A kind of liver magnetic resonance R2*Parameter drawing drawing method - Google Patents
A kind of liver magnetic resonance R2*Parameter drawing drawing method Download PDFInfo
- Publication number
- CN105608681B CN105608681B CN201610084868.3A CN201610084868A CN105608681B CN 105608681 B CN105608681 B CN 105608681B CN 201610084868 A CN201610084868 A CN 201610084868A CN 105608681 B CN105608681 B CN 105608681B
- Authority
- CN
- China
- Prior art keywords
- magnetic resonance
- liver
- parameter
- formula
- liver magnetic
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
- 210000004185 liver Anatomy 0.000 title claims abstract description 66
- 230000005291 magnetic effect Effects 0.000 title claims abstract description 45
- 238000000034 method Methods 0.000 title claims abstract description 37
- 230000003044 adaptive effect Effects 0.000 claims abstract description 11
- 238000005259 measurement Methods 0.000 claims abstract description 11
- 238000005192 partition Methods 0.000 claims abstract description 9
- 238000005457 optimization Methods 0.000 claims abstract description 7
- 238000009499 grossing Methods 0.000 claims description 5
- 238000002310 reflectometry Methods 0.000 claims description 4
- 241000208340 Araliaceae Species 0.000 claims 1
- 235000005035 Panax pseudoginseng ssp. pseudoginseng Nutrition 0.000 claims 1
- 235000003140 Panax quinquefolius Nutrition 0.000 claims 1
- 235000008434 ginseng Nutrition 0.000 claims 1
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 abstract description 26
- 229910052742 iron Inorganic materials 0.000 abstract description 13
- 238000009826 distribution Methods 0.000 abstract description 4
- 230000000694 effects Effects 0.000 description 4
- 238000000691 measurement method Methods 0.000 description 4
- 238000012986 modification Methods 0.000 description 4
- 230000004048 modification Effects 0.000 description 4
- 238000002592 echocardiography Methods 0.000 description 3
- 238000001914 filtration Methods 0.000 description 3
- 238000003745 diagnosis Methods 0.000 description 2
- 230000003902 lesion Effects 0.000 description 2
- 206010016654 Fibrosis Diseases 0.000 description 1
- 206010053159 Organ failure Diseases 0.000 description 1
- 208000002903 Thalassemia Diseases 0.000 description 1
- 208000007502 anemia Diseases 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000000740 bleeding effect Effects 0.000 description 1
- 239000008280 blood Substances 0.000 description 1
- 210000004369 blood Anatomy 0.000 description 1
- 230000007882 cirrhosis Effects 0.000 description 1
- 208000019425 cirrhosis of liver Diseases 0.000 description 1
- 230000008021 deposition Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 235000013399 edible fruits Nutrition 0.000 description 1
- 230000002124 endocrine Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 230000005350 ferromagnetic resonance Effects 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 238000011866 long-term treatment Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000009828 non-uniform distribution Methods 0.000 description 1
- 210000000056 organ Anatomy 0.000 description 1
- 239000000376 reactant Substances 0.000 description 1
- 238000000679 relaxometry Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/70—Denoising; Smoothing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10088—Magnetic resonance imaging [MRI]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20172—Image enhancement details
- G06T2207/20182—Noise reduction or smoothing in the temporal domain; Spatio-temporal filtering
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
- G06T2207/30056—Liver; Hepatic
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Magnetic Resonance Imaging Apparatus (AREA)
Abstract
The present invention proposes a kind of liver magnetic resonance R2*Parameter drawing drawing method, comprising the following steps: acquisition liver magnetic resonance image draws liver area-of-interest；For target voxel each in area-of-interest, the adaptive weighting of its neighboring voxels is obtained according to vector similitude measurement model formula；The R2 of each target voxel is obtained according to objective optimization Function Modules pattern and the first moment modular form of non-central card partition noise in conjunction with the deamplification and adaptive weighting of its neighboring voxels*Value, to obtain final liver magnetic resonance R2*Parameter Map.The present invention can effectively improve liver magnetic resonance R2*The spatial resolution and signal-to-noise ratio of Parameter Map can obtain more accurate liver iron distribution map.
Description
Technical field
The present invention relates to a kind of liver magnetic resonance R2*Parameter drawing drawing method.
Background technique
For transfusion-independence type patient, such as sickle anaemia and patients with thalassemia, long-term treatment of blood transfusion can be made
At excessive deposition of iron in the endocrine organs such as liver, heart, cirrhosis even organ failure can lead to.Due in human body
70%~90% excessive iron can be deposited in liver, usually using liver concentration of iron as an important finger of iron content in reactant
Mark.
Compared to the direct liver iron measurement method of percutaneous liver impedance rheograph, it is based on liver magnetic resonance R2*The liver iron of parameter is surveyed
Amount method can effectively avoid the complication such as liver bleeding.Utilize magnetic resonance R2*In the liver iron measurement method of parameter, it is based on liver magnetic
Resonate R2*The liver iron measurement method of Parameter Map, can react the distribution situation of iron in liver, and measurement accuracy will not be by
The factors such as sample size, position and liver iron uneven distribution influence, and clinically tool has great advantage.
Current liver ferromagnetic resonance R2*Parameter Map measurement method faces two challenges, and one is the liver magnetic resonance acquired
Echo noise is relatively low, leads to the liver magnetic resonance R2 of measurement*Parameter Map noise is relatively low；Another is curve matching
The selection of model, due to model of fit cannot well reaction signal evolution, cause measurement result inaccuracy, performance
For liver magnetic resonance R2*Parameter Map spatial resolution is low.So being carried out before thering is scholar to propose a kind of curve matching non local
Mean filter method (Feng Y, He T, Feng M, Carpenter J-P, Greiser A, Xin X, Chen W,
Pennell DJ,Yang G-Z,Firmin DN.Improved pixel-by-pixel MRI R2*relaxometry by
nonlocal means.Magn Reson Med 2014；72 (1): 260-268), this method includes two steps: to original magnetic resonance
Image carry out non-local mean filtering, to the magnetic resonance signal after denoising using non-central card partition noise first moment model into
The R2 of liver is calculated in row curve matching*Parameter Map.This method still uses the curve-fitting method by voxel, and first
The denoising operation of step improves the R2 being calculated to a certain extent*The signal-to-noise ratio of Parameter Map, but due to non-local mean
Filtering is a kind of nonlinear operation, can not restore actual signal completely in filtering, may cause the curve of second step
Fitting result spatial resolution reduces, and shows as R2*Parameter Map edge blurry has very big office for the diagnosis of local lesion
It is sex-limited.
Therefore, in view of the shortcomings of the prior art, providing one kind effectively improves liver magnetic resonance R2*Parameter Map spatial resolution with
And the method for signal-to-noise ratio comes accurately that measure liver iron very necessary.
Summary of the invention
The purpose of the present invention is to provide a kind of liver magnetic resonance R2*Parameter drawing drawing method solves existing test side
The R2 of method production*Parameter Map edge blurry has the problem of significant limitation for the diagnosis of local lesion.
In order to solve the above technical problems, the invention adopts the following technical scheme:
A kind of liver magnetic resonance R2*Parameter drawing drawing method, comprising the following steps:
Step 1 acquires liver magnetic resonance image, draws liver area-of-interest；
Step 2 obtains its neighbour according to vector similitude measurement model formula for target voxel each in area-of-interest
The adaptive weighting of domain voxel；
Step 3, in conjunction with the deamplification and adaptive weighting of its neighboring voxels, according to objective optimization Function Modules pattern
And the first moment modular form of non-central card partition noise, obtain the R2 of each target voxel*Value, to obtain final liver
Magnetic resonance R2*Parameter Map.
Further, in the step 1, it is total that liver magnetic is specifically obtained using the gradin-echo of more echo times
Shake image.
Further, in the step 2, vector similitude measurement model formula is
In formula, xjIt is with xiFor the search window Ω of center voxeliInterior neighboring voxels,It is voxel xjIn different echoes
The deamplification of time TEH is the size of smoothing parameter control weight.
Further, the size h of the smoothing parameter control weight is by constant beta and each channel Gauss of receiving coil
The standard deviation sigma of partition noiseg, utilize formula h=β σgIt obtains.
Further, in the step 3, objective optimization Function Modules pattern is
In formula, S0And R2*Respectively target voxel xiActual signal amplitude and effect transverse relaxation rate, f in TE=0 are
Parameter fitting model, and so on obtain the R2 of each target voxel*Value.
Further, in the step 3, the first moment modular form of non-central card partition noise is,
In formula, E () indicates expectation, SMFor the deamplification value of observation,！！For double factorial (i.e. n！！=n (n-2) (n-
4) ... 1), NRCFor receiving coil port number,1F1For confluent hypergeometric function.
Further, due in muting image background regions signal S0=0, the standard deviation sigmagBy formula) obtain.
Compared with prior art, the beneficial effects of the present invention are: liver magnetic resonance R2 can be effectively improved*The space of Parameter Map
Resolution ratio and signal-to-noise ratio can obtain more accurate liver iron distribution map.
Detailed description of the invention
Fig. 1 is a kind of liver magnetic resonance R2 of the present invention*The flow diagram of parameter drawing drawing method.
Fig. 2 is a kind of liver magnetic resonance R2 of the present invention*In parameter drawing drawing method one embodiment, two target voxels pair
The adaptive weighting figure of neighboring voxels in the search window for 11 × 11 sizes answered.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, with reference to the accompanying drawings and embodiments, right
The present invention is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, and
It is not used in the restriction present invention.
Fig. 1 shows a kind of liver magnetic resonance R2 of the present invention*A kind of one embodiment of parameter drawing drawing method: liver magnetic
Resonate R2*Parameter drawing drawing method, comprising the following steps:
Step 1 acquires liver magnetic resonance image, draws liver area-of-interest；
Step 2 obtains its neighbour according to vector similitude measurement model formula for target voxel each in area-of-interest
The adaptive weighting of domain voxel；
Step 3, in conjunction with the deamplification and adaptive weighting of its neighboring voxels, according to objective optimization Function Modules pattern
And the first moment modular form of non-central card partition noise, obtain the R2 of each target voxel*Value, to obtain final liver
Magnetic resonance R2*Parameter Map.
A kind of liver magnetic resonance R2 according to the present invention*One preferred embodiment of parameter drawing drawing method, the step 1
In, liver magnetic resonance image is specifically obtained using the gradin-echo of more echo times.
A kind of liver magnetic resonance R2 according to the present invention*Another preferred embodiment of parameter drawing drawing method, the step
In two, vector similitude measurement model formula is
In formula, xjIt is with xiFor the search window Ω of center voxeliInterior neighboring voxels,It is voxel xjIn different echoes
The deamplification of time TEH is the size of smoothing parameter control weight.
A kind of liver magnetic resonance R2 according to the present invention*Another preferred embodiment of parameter drawing drawing method, it is described smooth
The size h of state modulator weight is that (usually empirical value is considered constant in actual use, for example 1.5) and connects by constant beta
The standard deviation sigma of each channel Gaussian reflectivity mirrors of take-up circleg, utilize formula h=β σgIt obtains.
A kind of liver magnetic resonance R2 according to claim 1*Parameter drawing drawing method, it is characterised in that: the step
In rapid three, objective optimization Function Modules pattern is
In formula, S0And R2*Respectively target voxel xiActual signal amplitude and effect transverse relaxation rate, f in TE=0 are
Parameter fitting model, and so on obtain the R2 of each target voxel*Value.
A kind of liver magnetic resonance R2 according to the present invention*Another preferred embodiment of parameter drawing drawing method, the step
In three, the first moment modular form of non-central card partition noise is
In formula, E () indicates expectation, SMFor the deamplification value of observation,！！For double factorial (i.e. n！！=n (n-2) (n-
4) ... 1), NRCFor receiving coil port number,1F1For confluent hypergeometric function, confluent hypergeometric function1F1Acquisition can pass through three
The inquiry table that secondary spline interpolation carries out approximate foundation obtains, the inquiry table establish mode may is that it is logical to known receiving coil
Road number NRCConfluent hypergeometric function1F1Cubic spline interpolation is carried out, is established by interpolation knot and with interpolation subinterval one by one
The inquiry table that corresponding interpolating function coefficient is constituted.
A kind of liver magnetic resonance R2 according to the present invention*Another preferred embodiment of parameter drawing drawing method, due in nothing
The image background regions signal S of noise0=0, the standard deviation sigmagBy formula
It obtains.
Illustrate technical feasibility and technical effect of the invention as measurement object using human liver below:
The liver magnetic resonance image of 12 echoes, specific imaging parameters are as follows: when initial echo are acquired using gradin-echo
Between be 0.93ms, echo sounding 1.34ms, repetition time 200ms, thickness 10mm, average time 1, image array is big
Small is 64 × 128, and flip angle is 20 °；
The full liver area-of-interest of manual drawing.It should be noted that drawing the method for full liver area-of-interest and not only
It is limited only to manual drawing, can also be drawn using other methods automatically or semi-automatically；
Background area is chosen, according to formula (IV) estimating background noise comprising standard deviation sigmag.It should be noted that calculating ambient noise
The method of standard deviation is not limited only to this kind of method, can also estimate that only this kind of method is preferred using other methods
Method；
According to given receiving coil port number, cubic spline interpolation approximation is carried out to confluent hypergeometric function and is inquired
Table.The interpolation knot used is the interpolation knot of non-uniform Distribution；
As shown in Fig. 2, for each target voxel in area-of-interest, obtained according to formula (I) be with the target voxel
The adaptive weighting of neighboring voxels in the search window at center, the search window size specifically used is 11 × 11, constant beta=1.5.It needs
It is noted that the selection of constant beta is the empirical value obtained according to emulation experiment, which can this calculating time and final effect
Fruit；
The deamplification of all voxels in the adaptive weighting and search window obtained using step 2, according to formula (II)
And the R2 of each target voxel in area-of-interest is calculated in formula (III)*Value, obtains final liver area-of-interest R2*
Parameter Map.
Although reference be made herein to invention has been described for multiple explanatory embodiments of the invention, however, it is to be understood that
Those skilled in the art can be designed that a lot of other modification and implementations, these modifications and implementations will fall in this Shen
It please be within disclosed scope and spirit.More specifically, disclose in the application, drawings and claims in the range of, can
With the building block and/or a variety of variations and modifications of layout progress to theme combination layout.In addition to building block and/or layout
Outside the modification and improvement of progress, to those skilled in the art, other purposes also be will be apparent.
Claims (6)
1. a kind of liver magnetic resonance R2*Parameter drawing drawing method, it is characterised in that the following steps are included:
Step 1 acquires liver magnetic resonance image, draws liver area-of-interest；
Step 2 obtains its neighborhood body according to vector similitude measurement model formula for target voxel each in area-of-interest
The adaptive weighting of element；Vector similitude measurement model formula is
In formula, xjIt is with xiFor the search window Ω of center voxeliInterior neighboring voxels,It is voxel xjIn the different echo times
The deamplification of TE,H is the smoothing parameter for controlling the size of weight；
Step 3, in conjunction with the deamplification and adaptive weighting of the neighboring voxels of area-of-interest, according to objective optimization function
The first moment modular form of modular form and non-central card partition noise obtains the R2 of each target voxel in area-of-interest*Value,
To obtain final liver magnetic resonance R2*Parameter Map.
2. a kind of liver magnetic resonance R2 according to claim 1*Parameter drawing drawing method, it is characterised in that: the step 1
In, liver magnetic resonance image is specifically obtained using the gradin-echo of more echo times.
3. a kind of liver magnetic resonance R2 according to claim 1*Parameter drawing drawing method, it is characterised in that: the control
The smoothing parameter h of the size of weight is the standard deviation sigma by constant beta and each channel Gaussian reflectivity mirrors of receiving coilg, utilize public affairs
Formula h=β σgIt obtains.
4. a kind of liver magnetic resonance R2 according to claim 1*Parameter drawing drawing method, it is characterised in that: the step 3
In, objective optimization Function Modules pattern is
In formula, S0And R2*Respectively target voxel xiActual signal amplitude and effective transverse relaxation rate in TE=0, f are ginseng
Number model of fit, and so on obtain the R2 of each target voxel*Value.
5. a kind of liver magnetic resonance R2 according to claim 1*Parameter drawing drawing method, it is characterised in that: the step 3
In, the first moment modular form of non-central card partition noise is
In formula, E () indicates expectation, SMFor the deamplification value of observation,！！For double factorials, NRCFor receiving coil port number,1F1
For confluent hypergeometric function, σgFor the standard deviation of each channel Gaussian reflectivity mirrors of receiving coil, S0For background area signal, TE
For the echo time.
6. a kind of liver magnetic resonance R2 according to claim 1 or 5*Parameter drawing drawing method, it is characterised in that: due to
Muting image background regions signal S0=0, the standard deviation sigma of each channel Gaussian reflectivity mirrors of receiving coilgBy formulaIt obtains, in formula, E () indicates expectation, SMFor the deamplification value of observation,！！For
Double factorials, NRCFor receiving coil port number.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610084868.3A CN105608681B (en) | 2016-02-14 | 2016-02-14 | A kind of liver magnetic resonance R2*Parameter drawing drawing method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610084868.3A CN105608681B (en) | 2016-02-14 | 2016-02-14 | A kind of liver magnetic resonance R2*Parameter drawing drawing method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105608681A CN105608681A (en) | 2016-05-25 |
| CN105608681B true CN105608681B (en) | 2018-12-21 |
Family
ID=55988597
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610084868.3A Expired - Fee Related CN105608681B (en) | 2016-02-14 | 2016-02-14 | A kind of liver magnetic resonance R2*Parameter drawing drawing method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105608681B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102016209885B4 (en) | 2016-06-06 | 2022-03-10 | Siemens Healthcare Gmbh | Automatic characterization of liver tissue |
| CN113240078B (en) * | 2021-04-26 | 2024-03-19 | 南方医科大学 | Magnetic resonance R2 based on deep learning network * Parameter quantization method, medium and device |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103218788A (en) * | 2013-04-24 | 2013-07-24 | 南方医科大学 | Method for measuring liver magnetic resonance crosswise relaxation rate R2* parameter |
| CN103714521A (en) * | 2013-12-30 | 2014-04-09 | 南方医科大学 | Liver R2* graph measuring method based on inquiry table |
-
2016
- 2016-02-14 CN CN201610084868.3A patent/CN105608681B/en not_active Expired - Fee Related
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103218788A (en) * | 2013-04-24 | 2013-07-24 | 南方医科大学 | Method for measuring liver magnetic resonance crosswise relaxation rate R2* parameter |
| CN103714521A (en) * | 2013-12-30 | 2014-04-09 | 南方医科大学 | Liver R2* graph measuring method based on inquiry table |
Non-Patent Citations (3)
| Title |
|---|
| A Novel Semiautomatic Parenchyma Extraction Method for Improved MRI R2* Relaxometry of Iron Loaded Liver;Yanqiu Feng 等;《JOURNAL OF MAGNETIC RESONANCE IMAGING》;20141231;第40卷;正文第68页右栏第3段，第69右栏第2-3段，第70页左栏第6-7段，第70页右栏第1-6段，图3 * |
| Improved Pixel-by-Pixel MRI R2* Relaxometry by Nonlocal Means;Yanqiu Feng 等;《Magnetic Resonance in Medicine》;20141231;第72卷;第260-268页 * |
| Yanqiu Feng 等.A Novel Semiautomatic Parenchyma Extraction Method for Improved MRI R2* Relaxometry of Iron Loaded Liver.《JOURNAL OF MAGNETIC RESONANCE IMAGING》.2014,第40卷正文第68页右栏第3段，第69右栏第2-3段，第70页左栏第6-7段，第70页右栏第1-6段，图3. * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN105608681A (en) | 2016-05-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111656392B (en) | System and method for synthesizing magnetic resonance images | |
| Rajan et al. | Noise measurement from magnitude MRI using local estimates of variance and skewness | |
| Belaroussi et al. | Intensity non-uniformity correction in MRI: existing methods and their validation | |
| US9542763B2 (en) | Systems and methods for fast reconstruction for quantitative susceptibility mapping using magnetic resonance imaging | |
| US7782056B2 (en) | Systems and methods for correction of inhomogeneities in magnetic resonance images | |
| Rajan et al. | A new non-local maximum likelihood estimation method for Rician noise reduction in magnetic resonance images using the Kolmogorov–Smirnov test | |
| Jog et al. | Improving magnetic resonance resolution with supervised learning | |
| CN103632345B (en) | A kind of MRI image inhomogeneity correction method based on regularization | |
| US10698065B2 (en) | System, method and computer accessible medium for noise estimation, noise removal and Gibbs ringing removal | |
| CN108778116A (en) | MR imaging apparatus and image processing method | |
| DE102014223734B4 (en) | Correction of imaging techniques in a magnetic resonance device | |
| Lugauer et al. | Robust spectral denoising for water-fat separation in magnetic resonance imaging | |
| CN105608681B (en) | A kind of liver magnetic resonance R2*Parameter drawing drawing method | |
| Feng et al. | Improved pixel‐by‐pixel MRI R2* relaxometry by nonlocal means | |
| CN109146802B (en) | Method and device for correcting uniformity of magnetic resonance image | |
| Kafali et al. | Phase‐correcting non‐local means filtering for diffusion‐weighted imaging of the spinal cord | |
| US20160054420A1 (en) | Compensated magnetic resonance imaging system and method for improved magnetic resonance imaging and diffusion imaging | |
| Gan et al. | SS-JIRCS: Self-supervised joint image reconstruction and coil sensitivity calibration in parallel MRI without ground truth | |
| CN103714521B (en) | Liver R2* figure measuring method based on inquiry table | |
| WO2017120567A1 (en) | System and method for producing high-resolution magnetic resonance relaxation parameter maps | |
| Pieciak et al. | Bias correction for non-stationary noise filtering in MRI | |
| Ji et al. | A novel, fast entropy-minimization algorithm for bias field correction in MR images | |
| Liu et al. | A vectorized Levenberg-Marquardt model fitting algorithm for efficient post-processing of cardiac T1 mapping MRI | |
| Yaghoobi et al. | De-noising of 3D multiple-coil MR images using modified LMMSE estimator | |
| KR101418170B1 (en) | Apparatus and method of magnetic resonance imaging acquisition |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20181221 |